1
Heuristic Algorithms for the Routing and
Wavelength Assignment of Scheduled Lightpath
Demands in Optical Networks
Nina SkorinKapov
Abstract— This paper addresses the problem of routing and
wavelength assignment (RWA) of scheduled lightpath demands
(SLDs) in wavelength routed optical networks with no wave
length converters.The objective is to minimize the number of
wavelengths used.This problem has been shown to be NP
complete so heuristic algorithms have been developed to solve
it suboptimally.Suggested is a tabu search algorithm along with
two simple and fast greedy algorithms for the RWASLDproblem.
We compare the proposed algorithms with an existing tabu search
algorithm for the same problem and with lower bounds derived
in this paper.Results indicate that the suggested algorithms not
only yield solutions superior in quality to those obtained by the
existing algorithm,but have drastically shorter execution times.
Index Terms— Routing and wavelength assignment,scheduled
lightpath demands,optical networks,tabu search
I.INTRODUCTION
W
AVELENGTHrouted WDM(wavelength division mul
tiplex) optical networks can exploit the large band
width of optical ﬁbers by dividing it among different wave
lengths.These networks can also connect a large number
of nodes using a small number of wavelengths by taking
advantage of spatial wavelength reusability.Wavelength routed
WDM networks are equipped with conﬁgurable WDM nodes
which enable us to set up and tear down alloptical connec
tions,called lightpaths,between pairs of nodes.These all
optical connections can traverse multiple physical links in the
network.Information sent via a lightpath does not require any
optoelectronic conversion at intermediate nodes.
Establishing a set of lightpaths creates a virtual topology on
top of the physical topology.The physical topology represents
the physical interconnection of WDM nodes by actual ﬁber
links in the WDM optical network.The links in the virtual
topology represent alloptical connections or lightpaths estab
lished between pairs of nodes.Demands to set up lightpaths
between certain nodes can be static,scheduled or dynamic.In
the case of static lightpath demands,a desired static virtual
topology (i.e.the set of lightpaths we wish to establish) is
known a priori.Such a virtual topology is set up ‘permanently’
which implies that even when a certain connection is not
being used its resources remain reserved.When we refer to
scheduled lightpath demands we refer to connection requests
for which we know the setup and teardown times a priori.
In other words,we know in advance when a connection
N.SkorinKapov is with the Department of Telecommunications,Faculty of
Electrical Engineering and Computing,University of Zagreb,Zagreb,Croatia.
(email:nina.skorinkapov@fer.hr).
will be needed.For dynamic lightpath demands,lightpath
requests arrive unexpectedly with randomholding times.These
lightpaths are set up dynamically at request arrival time and
are released when the connection is terminated.
To set up a lightpath,nodes on its corresponding physical
path must be conﬁgured to do so.If the network lacks wave
length converters,the lightpath must use the same wavelength
along its entire physical path.This is called the wavelength
continuity constraint.Two lightpaths that share a common
physical link cannot be assigned the same wavelength.This
is called the wavelength clash constraint.The source and
destination nodes of the lightpath must also have available
transmitters and receivers respectively in order for the lightpath
to be successfully set up.
Determining routes for a set of lightpath demands and
assigning wavelengths to these routes subject to a subset of
the above mentioned constraints is known as the Routing
and Wavelength Assignment (RWA) problem.This problem
has been proven to be NPcomplete [1] and several heuristic
algorithms have been developed to solve it suboptimally [2]
[3] [4] [5].Several variations of the RWA problem have
been studied [6] [7] [8] such as the routing and wavelength
assignment of static,scheduled or dynamic lightpath demands
with a limited or unlimited number of wavelengths in networks
with wavelength converters at each node,at a subset of nodes,
or in networks with no wavelength converters.The objective
is often to minimize the number of wavelengths used or to
maximize the number of lightpaths set up subject to a limited
number of wavelengths.
In this paper we consider the Routing and Wavelength
Assignment of Scheduled Lightpath Demands (RWA SLD)
in networks with no wavelength converters.We assume no
limit on the number of transmitters and receivers at each node
and the number of available wavelengths on each link.The
objective is to minimize the number of wavelengths needed
to successfully establish a desired set of scheduled lightpath
demands.Considering scheduled lightpath demands seems
relevant due to the periodic nature of trafﬁc [9].We know
that trafﬁc between some nodes (e.g.ofﬁce headquarters) is
heavier during ofﬁce hours than in the middle of the night,and
vice versa for the other nodes (e.g.networked data bases).We
could utilize this information by setting up multiple lightpaths
between nodes at times when their trafﬁc is heavy and tearing
down some or all of these lightpaths at times when their trafﬁc
is low.By tearing down lightpaths between nodes at times of
low trafﬁc,their resources are freed and can thus be used to
2
establish alternative connections.If we have lightpath demands
which do not overlap in time and if we take this information
into consideration when performing routing and wavelength
assignment,we can route both demands on the same path
using the same wavelength without them clashing.This can
signiﬁcantly reduce the amount of network resources required
to successfully route a set of lightpath demands.
Since the Routing and Wavelength Assignment of Sched
uled Lightpath Demands is solved a priori using given
scheduling information,the lightpaths can be set up and torn
down quickly at the speciﬁed times.This is an advantage over
the routing and wavelength assignment of dynamic lightpath
demands where RWA is performed dynamically as lightpath
requests arrive,resulting in longer setup delays.The RWA
SLD problem has not been studied as widely as the static and
dynamic cases.A branch and bound algorithm along with a
tabu search heuristic algorithm are given in [9].In this work
we suggest a faster and more efﬁcient tabu search algorithm
for the RWA SLD problem along with two very fast and
simple greedy algorithms based on edge and time disjoint
paths.Furthermore,we derive an efﬁcient lower bound on the
number of wavelengths needed for the RWA SLD problem.
The rest of the paper is organized as follows.In Section II,
we formally deﬁne the RWA SLD problem.Related work is
brieﬂy described in Section III.In Section IV we suggest a
tabu search algorithm for the RWA problem,followed by two
simple yet very efﬁcient greedy algorithms in Section V.In
VI we discuss lower bounds.Numerical results and concluding
remarks are given in Sections VII and VIII,respectively.
II.PROBLEM DEFINITION
The physical optical network is modelled as a graph G =
(V;E),where V is the set of nodes and E is the set of edges.
Edges are assumed to be bidirectional (each representing a pair
of optical ﬁbers,i.e.one ﬁber per direction) and have assigned
weights representing their length or cost.Given is a set of
scheduled lightpath demands ¿ = fSLD
1
;:::;SLD
M
g.Each
scheduled lightpath demand SLD
i
,where i = 1;:::;M,
is represented by a tuple (s
i
;d
i
;n
i
;®
i
;!
i
) as suggested in
[9].Here s
i
;d
i
2 V,are the source and destination nodes
of SLD
i
,n
i
is the the number of requested lightpaths
between these nodes,and ®
i
and!
i
are the setup and
teardown times respectively.The Routing and Wavelength
Assignment problem consists of ﬁnding a set of paths P =
fP(SLD
1
);:::;P(SLD
M
)g in G,each corresponding to one
scheduled lightpath demand,and assigning a set of wave
lengths to each of these paths.As in [9],we assume that all
the lightpaths of a particular SLD must be routed on the same
path and must therefore be assigned different wavelengths.We
will refer to this as the group lightpath constraint.As a result,
each path P(SLD
i
),where i = 1;:::;M,must be assigned
a set of n
i
wavelengths,one for each individual lightpath of
SLD
i
.The set of wavelengths assigned to paths P(SLD
i
) and
P(SLD
j
),where i 6= j and i;j = 1;:::;M,must be disjoint
if these paths share a common edge and if SLD
i
and SLD
j
overlap in time.The objective is to minimize the number of
wavelengths assigned and required to successfully route and
assign wavelengths to all the scheduled lightpath demands in
¿.
III.RELATED WORK
In [9],the authors solve the Routing and Wavelength
Assignment problem of Scheduled Lightpath Demands by
decoupling it into two separate subproblems:routing and
wavelength assignment.They suggest a branch and bound
algorithm for the routing subproblem which provides optimal
solutions but has an exponential complexity.To solve the
routing subproblem for larger problems,the authors propose a
tabu search algorithmwhich obtains suboptimal solutions.Two
different optimization criteria are considered giving rise to two
versions of the tabu search algorithm:TS
ch
and TS
cg
.The
TS
ch
algorithm minimizes the number of WDM channels
1
which is particularly important in opaque WDMnetworks and
will not be discussed here.The TS
cg
algorithm minimizes
congestion,i.e.the number of lightpaths on the most heavily
loaded link.This optimization criterion is important in net
works with a limited number of wavelengths since congestion
is essentially a lower bound on the number of wavelengths
required.Wavelength assignment is performed subsequently
using a greedy graph coloring algorithm (referred to as GGC)
suggested in [10].The objective of the GGC algorithm is to
minimize the number of wavelengths used.The quality of the
solutions for the RWA SLD problem obtained by TS
cg
=GGC
are measured in terms of the number of wavelengths needed.
A complete description is provided in [9].
Fault tolerant routing and wavelength assignment of sched
uled lightpath demands was studied in [11] and [12].In
[11],the authors formulate the problem of fault tolerant
RWA SLD with the objective being to minimize the number
of WDM channels.They propose a Simulated Annealing
algorithm using channel reuse and backup multiplexing.In
[12],fault tolerant RWA SLD under single component failure
is considered.The authors develop ILP formulations for the
problem with dedicated and shared protection.Two objectives
are considered:minimizing the capacity needed to guarantee
protection for all connection requests and maximizing the
number of requests accepted subject to a limited capacity.
IV.AN ALTERNATIVE TABU SEARCH ALGORITHM FOR
THE ROUTING SUBPROBLEM
A.Tabu Search
Tabu search is an iterative metaheuristic which guides
simpler heuristics in such a way that they explore various
areas of the solution space and prevents them from remaining
in local optima.In every iterative step of the tabu search
method,we begin with some current solution and explore
its neighboring solutions.Neighboring solutions with respect
to the current one are all those obtained by applying some
elementary transformation to the current solution.The best
neighboring solution according to some evaluation function
is selected as the new current solution in the next iteration.
1
WDM channels refer to the use of a particular wavelength on a directed
physical link and are speciﬁed by the wavelength used and the head and tail
nodes of the directed link.
3
After executing a desired number of iterations,the best found
solution overall,called the incumbent solution,is deemed the
ﬁnal result.
To prevent the search technique from getting stuck in a
local optimum or cycling between already seen solutions,a
memory structure called a tabu list is introduced.The tabu list
‘memorizes’ a certain number of previously visited solutions
which are then forbidden for as long as they remain in the
list.The tabu list is updated circularly after every iteration
by adding the current solution to the list and removing the
oldest element if the list is full.The length of the tabu
list can vary depending on different problems and is often
determined experimentally.The key to developing a good
tabu search algorithm is to deﬁne a good initial solution,
neighborhood structure and evaluation function.Sometimes
the neighborhood of a solution can be very large so various
neighborhood reduction techniques are applied.As a result,
only a subset of the neighboring solutions are evaluated.A
detailed description of the tabu search method can be found
in [13].
B.The proposed tabu search algorithm:TS
cn
The tabu search algorithm TS
cg
for the routing subproblem
suggested by the authors of [9],although faster than their
branch and bound algorithm,still has a fairly long execution
time if run for a large enough neighborhood size and the
number of iterations needed to obtain solutions of good
quality.This is due to their randomized neighborhood search
technique.Namely,the TS
cg
algorithm begins by computing
the Kshortest paths between the source and destination nodes
of each of the M SLDs in ¿.Potential routing solutions
are represented by a vector of M integers (initially all set
to 1) ranging from 1 to K,each representing the path used
by a particular SLD.Neighboring solutions are all those in
which one and only one SLD is routed on a different route.A
neighborhood deﬁned as such can be very large so a desired
number of SLDs are chosen at random,randomly rerouted
and evaluated according to their corresponding congestion.To
obtain good solutions,a large number of iterations and a fairly
large number of neighbors need to be evaluated.
Since our ﬁnal objective for the RWA SLD problem is to
minimize the number of wavelengths used,we will compare
our results with that of algorithm TS
cg
=GGC.The evaluation
function used by TS
cg
(i.e.minimization of congestion) does
not necessarily lower the number of wavelengths needed even
though this is the ﬁnal goal of the TS
cg
=GGC algorithm.
Recall that by minimizing congestion,which is the number of
lightpaths on the most loaded link,we are essentially trying
to minimize the lower bound on the number of wavelengths
needed.Minimizing the lower bound does not necessarily
mean that the number of wavelengths needed will be lower.
On the other hand,if we tried to minimize the upper bound on
the number of wavelengths needed,this guarantees that there
exists a wavelength assignment with at most the upper bound
number of wavelengths.
The tabu search algorithm for the routing subproblem
suggested in this work attempts to improve the drawbacks
mentioned above.A new evaluation function is suggested
along with a directed neighborhood search technique which
drastically reduces the neighborhood size.As a result,this
algorithm,in combination with the same graph coloring algo
rithm (GGC) used in [9] for wavelength assignment,performs
faster and obtains solutions of better or equal quality than
those obtained by TS
cg
=GGC.We will refer to the proposed
algorithm as TS
cn
where cn stands for chromatic number.
The relevance of this name will be described below.
1) Preliminaries:
Recall that the objective of the wave
length assignment algorithm as well as our ﬁnal objective
for the RWA SLD problem is to minimize the total number
of wavelengths used.Since we perform wavelength assign
ment using the GGC algorithm after solving the routing
subproblem,it seems that having a routing algorithm aware
of the objective and behavior of the wavelength assignment
algorithm could help to obtain better solutions for the RWA
SLD problem.In other words,the optimization criteria of
the routing algorithm should be such that it gives routing
schemes on which wavelength assignment can be performed
using a smaller number of wavelengths.To formulate such an
optimization criteria we must ﬁrst analyze the behavior of the
wavelength assignment algorithm which is here essentially a
graph coloring algorithm.
Namely,the problem of wavelength assignment can be
reduced to the graph coloring problem which consists of
assigning colors to the nodes of a graph such that no two
neighboring nodes are assigned the same color.The objective
is to minimize the total number of colors used.This classical
graph theory problem has been proven to be NPcomplete so
several heuristic algorithms have been developed [14].One
such algorithm is the GGC algorithm proposed in [10].The
routing solution obtained by solving the routing subproblem
is used as input for the GGC algorithm in the following
manner.A conﬂict graph corresponding to the routing solution
is created where each established lightpath is represented
by one node in the conﬂict graph
2
and there is an edge
between two nodes if their respective paths share a common
physical link in G and overlap in time.This means that the
lightpaths corresponding to neighboring nodes in the conﬂict
graph cannot be assigned the same wavelength.The graph
coloring algorithm GGC is executed on this conﬂict graph
where each color represents a different wavelength.
The minimum number of colors needed to color a graph is
called the chromatic number.In 1941,Brooks [15] showed the
upper bound on the chromatic number to be ¢(G) +1,where
¢(G) is the maximum degree in G.This bound was used
for a long time.A more recent result by L.Stacho in 2001
[16] gives a tighter upper bound.The author showed that the
chromatic number is always less than or equal to ¢
2
(G) +1
where ¢
2
(G) is the largest degree of any node v in G,such
that v is adjacent to a node whose degree is at least as big as
its own.
Let us consider an example.In Fig.1,two simple 4 node
2
Note that one node in the conﬂict graph represents one particular lightpath
of an SLD and not the SLD itself.In other words,each scheduled lightpath
demand SLD
i
is represented by n
i
nodes in the conﬂict graph which are all
adjacent to each other.
4
1
3
4
2
1
3
4
2
(a) (b)
Fig.1.Two simple 4 node networks.
networks are shown.For the network shown in Fig 1.(a),
both Brooks’ and Stacho’s upper bounds give a value of 3.
However,for the network shown in Fig.1.(b),using Brooks’
upper bound on the chromatic number,we get a value of
¢(G) +1 = 3 +1 = 4,while using Stacho’s we get a value
of ¢
2
(G) +1 = 1+1 = 2.We can easily see that nodes 1,2,
and 4 can be colored with one color and node 3 with a second
color.
According to Stacho’s upper bound,it is evident that graphs
with smaller values of ¢
2
(G) give smaller upper bounds
for the chromatic number.Note that a routing solution X
obtained by solving the routing subproblem corresponds to
exactly one conﬂict graph CG(X) on which we solve the
graph coloring problem.If we take into consideration upon
constructing routing solution X that we wish to minimize
its corresponding value for ¢
2
(CG(X)),we may attain a
routing scheme whose corresponding conﬂict graph will need
fewer colors to perform graph coloring successfully.This also
means that we need fewer wavelengths to perform a successful
wavelength assignment.Accordingly,the optimization criteria
or evaluation function used by the TS
cn
algorithm to evaluate
a routing solution X is the minimization of the upper bound
on the chromatic number of its corresponding conﬂict graph
(i.e.min (¢
2
(CG(X)) +1)).
2) The TS
cn
algorithm:
A description of the tabu search
algorithmTS
cn
proposed for the routing subproblemof sched
uled lightpath demands follows.As in [9],we ﬁrst compute
the Kshortest paths between each sourcedestination pair of
each SLD using Eppstein’s algorithm [17].K can be set to
various values.If we set K to a larger value,the solution
obtained will probably need less wavelengths but the physical
paths used to route the SLDs will probably be longer.This may
present a problem if delay is an issue.On the other hand,if K
is set to a smaller value,the physical paths will be restricted
to only a few of the shortest paths.As a result,the number of
wavelengths needed to successfully route the SLDs will most
likely be larger.
Recall that we have given a graph G = (V;E) and a set
of M Scheduled Lightpath Demands (SLDs) each represented
by a tuple (s
i
;d
i
;n
i
;®
i
;!
i
),where s
i
is the source,d
i
is the
destination,n
i
is the number of requested lightpaths,®
i
is
the setup time,and!
i
is the teardown time of the SLD.
For simpliﬁcation purposes,the authors of [9] assume that
the group lightpath constraint applies,i.e.all the lightpaths
of a particular SLD are routed on the same path.The same
will be assumed here for easier comparison of the mentioned
algorithms.A potential routing solution X is represented by
a vector of M integers,X = (x
1
;:::;x
M
),where x
i
2
f1;:::;Kg;i = 1;:::;M,represents the path used by SLD
i
.
If the integer representing the path of a speciﬁc SLD is set to
1,that means that that particular SLD is routed on the shortest
path from its source to destination.If it is set to 2,then that
SLD is routed on the second shortest path from source to
destination,and so on up to the K
th
shortest path.The TS
cn
algorithm initially routes all the SLDs in ¿ on their shortest
paths in G.
Neighboring solutions with respect to the current one are
all those where one and only one SLD is routed on a different
route.Instead of selecting a large number of neighbors at
random as in [9] and evaluating them,we suggest a more
directed neighborhood reduction technique.This technique
drastically reduces the size of the neighborhood and yet helps
obtain solutions of good quality.First we construct the conﬂict
graph CG(X) of the current solution X and then ﬁnd the set
of nodes L(X) which determine ¢
2
(CG(X)).That is,we
ﬁnd the one or more nodes which have the largest degree
in CG(X),subject to the fact that they are adjacent to a
node whose degree is at least as big as their own.Recall that
the nodes in the conﬂict graph represent individual lightpaths
and not SLDs.Since we are routing all the lightpaths of a
particular SLD on the same path (i.e.they all have the same
degree
3
),either all or none of the lightpaths of a particular
SLD are in L(X).As a result,we can easily reduce the
set of lightpaths L(X) to their corresponding set of SLDs
L
SLD
(X) where jL
SLD
(X)j · jL(X)j.The number of SLDs
in L
SLD
(X) is usually fairly small.Instead of evaluating
a huge number of neighboring solutions,we evaluate only
jL
SLD
(X)j neighbors.The jL
SLD
(X)j neighbors are obtained
by randomly rerouting each SLD in L
SLD
(X).
To determine the best neighboring solution which will
pass into the next iteration,we create a conﬂict graph for
each neighboring solution and ﬁnd its corresponding upper
bound on the chromatic number.In other words,we ﬁnd
¢
2
(CG(X)) + 1 for each neighbor X.The neighboring
solution with the lowest upper bound is passed into the next
iteration and becomes the new current solution.If this solution
is better than the incumbent solution,the incumbent solution
is updated.Such an evaluation function is the motivation for
the neighborhood reduction technique.Namely,if we reroute
the SLDs which determine ¢
2
(CG(X)) (i.e.the SLDs in
L
SLD
(X)) instead of rerouting SLDs at random,there is a
greater chance that we might improve the upper bound and
pass a better solution into the next iteration.Of course,this
does not guarantee that a better solution cannot be found by
rerouting a series of SLDs not included in set L
SLD
(X).
However,this is an approximation algorithm in which a trade
3
For example,if SLD
1
with n
1
= 3 lightpaths is adjacent to SLD
2
and
SLD
3
with n
2
= 7 and n
3
= 5 lightpaths respectively,all three nodes
representing lightpaths of SLD
1
have a degree of n
2
+n
3
+(n
1
¡1) =
7 +5 +(3 ¡1) = 14 in the conﬂict graph.n
1
¡1 is added because each
lightpath of SLD
1
is adjacent to all the other lightpaths of SLD
1
except
itself.
5
off between execution time and potential solution quality must
be made.
A few extra features of the algorithm are as follows.
For diversiﬁcation purposes,if there is no improvement
after a certain number of iterations,we take a random
number of SLDs and randomly reroute them.If at some
point no neighbor can be rerouted (basically,they have all
been rerouted and are on the tabu list),we reroute all the
SLDs with the maximum degree in the conﬂict graph,(i.e.
¢(CG(X)),not ¢
2
(CG(X))).If this solution is not on the
tabu list,it becomes the new current solution.If it is on the
tabu list,we take a random number of SLDs and randomly
reroute them.In addition to the tabu list which records the
last change made in the form of (SLD
i
;P(SLD
i
)),where
SLD
i
is a number ranging from 1 to M and P(SLD
i
) is a
number ranging from 1 to K,we separately record the SLD
which was last changed.Rerouting this SLD on any path is
forbidden in the following iteration.
The pseudocode of TS
cn
follows.
Input and initialization:
G = (V;E);
¿ = fSLD
1
;:::;SLD
M
g,where SLD
i
= (s
i
;d
i
;n
i
;®
i
;!
i
);i =
1;:::;M;//the set of SLDs
K;//the number of Kshortest paths
//initial routing solution with all paths set to 1
X
0
= (x
0
1
;:::;x
0
M
);x
0
i
:= 1;i = 1::::;M;
Find ¢
2
(CG(X
0
)) and the corresponding SLDs L
SLD
(X
0
) =
fSLD
r
1
;:::;SLD
r
s
g;r
i
2 f1;:::;Mg;i = 1;:::;s;
X:= X
0
;//incumbent solution
¢
2
:= ¢
2
(CG(X
0
));//ﬁtness of incumbent solution
Tabulist:= fg;i:= 0;itWOImprovenment:= 0;
Begin:
//iterations
while i < desired number of iterations do
X
it
:= fg,¢
2
(CG(X
it
)):= 1,L
SLD
(X
it
):= fg;
for j in 1;:::;jL
SLD
(X
i
)j do
x
i
r
j
0
:=random number in f1;:::;Kgnx
i
r
j
except for that forbidden
by tabu list;
X
0
i
:= (x
i
1
;:::;x
i
r
j
¡1
;x
i
r
j
0
;x
i
r
j
+1
;:::;x
i
M
);
Find ¢
2
(CG(X
0
i
)) and L
SLD
(X
0
i
);
if ¢
2
(CG(X
0
i
)) < ¢
2
(CG(X
it
)) then
X
it
:= X
0
i
,¢
2
(CG(X
it
)):= ¢
2
(CG(X
0
i
)),L
SLD
(X
it
):=
L
SLD
(X
0
i
);
end if
end for
if ¢
2
(CG(X
it
)) == 1then
//all neighbors are on the tabu list
Find all nodes with max degree in conﬂict graph of solution X
i
(i.e.
¢(CG(X
i
))) and randomly reroute them.If this is on tabu list,choose
a random number of SLDs and randomly reroute them;
else
X
i
:= X
it
,¢
2
(CG(X
i
)):= ¢
2
(CG(X
it
)),L
SLD
(X
i
):=
L
SLD
(X
it
);
end if
Update tabu list;
if ¢
2
(CG(X
i
)) < ¢
2
then
X:= X
i
,¢
2
:= ¢
2
(CG(X
i
));
else
itWOImprovement:= itWOImprovement +1;
end if
if itWOImprovement ¸ allowed no.of iterations without improve
ment then
Select a random number of SLDs and randomly reroute them;
end if
i:= i +1;
end while
End
After solving the routing subproblem with the TS
cn
al
gorithm,we use the GGC graph coloring algorithm [10]
for wavelength assignment.The computational results are
presented in Section VII.
C.Complexity Analysis
For better insight,we examine the computational complex
ity of the TS
cg
=GGC and TS
cn
=GGC algorithms.Both tabu
search algorithms use Eppstein’s algorithm for computing the
kshortest paths,run the desired number of iterations of their
respective tabu search algorithms,and then use the GGC
algorithm for wavelength assignment.As a result,the com
putational complexity of the TS
cg
=GGC and TS
cn
=GGC
algorithms differ only with respect to the operations performed
in each iteration of the tabu search algorithms.Eppstein’s
algorithm for the Kshortest paths with time complexity
O(jEj+jV j log jV j+K)) is run for each of the M SLDs.The
GGC algorithm is an improvement algorithm which is run for
a desired number of iterations where each iteration has a worst
case time complexity of O(jV j
2
).The complexity analysis of
the iterations of the respective tabu search algorithms follows.
In each iteration of the TS
cg
=GGC algorithm,each neigh
boring solution is evaluated by ﬁnding the highest congestion
on any of the jEj links.The congestion on edge e 2 E is
computed by sorting the setup and teardown times of the
SLDs routed over e and then ﬁnding the time interval in
which the maximum number of lightpaths are active.Sorting
takes O(Mlog M) time.Finding the highest congestion takes
O(M) time since the number of time intervals must be · 2M.
It follows that ﬁnding the highest congestion over all edges
takes O(jEjM(log M+1)) time.Time complexity analysis for
some of these steps was developed in [18] for their Simulated
Annealing algorithm for faulttolerant RWA SLD.If Nbr is
the neighborhood size,each of the Nbr neighbors is evaluated
in O(jEjM(log M +1)) time in each iteration.
The TS
cn
=GGC algorithm,on the other hand,evaluates
each neighboring solution X by constructing a conﬂict graph
CG(X) and then ﬁnding the upper bound on the chromatic
number,¢
2
(CG(X)) +1,of the conﬂict graph.The conﬂict
graph can be constructed in O(M
2
) time.¢
2
(CG(X)) and
the corresponding neighborhood L
SLD
(X),can be found
in O(M
2
).It follows that the complexity of evaluating a
neighboring solution is O(M
2
).Since the neighborhood is
adaptive,the size of the neighborhood is not constant.The
upper bound on the number of neighbors is M.This occurs
only if the conﬂict graph CG(X) is a complete graph.
However,empirical testing indicates that the neighborhood
size is often drastically smaller than M (see Section VII,
Table V),even when M is large.The neighborhood size could
also be additionally upper bounded by a constant,say value
Nbr used by TS
cg
=GGC,so that the number of neighbors
evaluated in each iteration is minfL
SLD
(X);Nbrg,where
each evaluation is performed in O(M
2
) time.The numerical
results in Section VII indicate that TS
cg
=GGC is signiﬁcantly
slower than TS
cn
=GGC for the cases tested.
6
V.EDGE AND TIME DISJOINT PATH ALGORITHMS:
DP
RWA
SLD AND DP
RWA
SLD
¤
A.DP
RWA
SLD
In order to solve the routing and wavelength assignment
problem of a set of scheduled lightpath demands,we pro
pose an algorithm motivated by a routing and wavelength
assignment algorithm for static lightpath demands suggested
in [4].This algorithm,called Greedy
EDP
RWA,creates a
partition ¿
1
;:::;¿
k
of a set of static lightpath demands
4
¿ =
f(s
i
;d
i
);:::;(s
M
;d
M
)g,where s
i
;d
i
2 V;i = 1;:::;M.
Each element of the partition is composed of a subset of
lightpath demands which can be routed on mutually edge
disjoint paths in G and hence can be assigned the same
wavelength.The length of each path is upper bounded by a
value h set in [4] to max(diam(G);
p
jEj).The justiﬁcation
for setting h to this value is given in [19].The number of
distinct wavelengths needed to successfully perform RWA
corresponds to the number of elements in the partition.
To solve the RWA SLD problem,we propose a fast algo
rithm using some of the ideas introduced above.Routing and
wavelength assignment are solved simultaneously based on the
idea of ﬁnding a partition ¿
1
;:::;¿
k
of the set of scheduled
lightpath demands ¿ where each element ¿
i
;i 2 1;:::;k,of
the partition is composed of SLDs routed over ‘disjoint’ paths.
Here,‘disjoint’ paths include not only edge disjoint paths as in
Greedy
EDP
RWA,but time disjoint paths as well.Two paths
that are disjoint in time may be routed using the same physical
edges.The lengths of the paths are upper bounded by a value
h
.We will refer to this algorithm as
DP
RWA
SLD
,where
DP stands for Disjoint Paths.
The DP
RWA
SLD algorithm ﬁrst sorts the SLDs in ¿
in decreasing order of the number of lightpaths each SLD
requests.The reason for this will be discussed later.A partition
of ¿ is then created in the following manner.The ﬁrst SLD
from the sorted set of demands is routed on its shortest path
in G.This SLD and its corresponding path are placed in ¿
1
and removed from ¿.Subsequent attempts are made to route
the remaining requests in ¿ as follows.For each new SLD
considered,the edges of the paths of those SLDs already in ¿
1
with which the new SLD overlaps in time are deleted from G.
The resulting graph is referred to as G
0
.The new SLD is now
routed on its shortest path in G
0
.If this routing is successful
(i.e.there exists such a path in G
0
whose length is · h),the
new SLD is added to ¿
1
and removed from ¿.Otherwise,it
remains in ¿.After attempting to route all the SLDs in ¿,
we are left with a set of demands routed on mutually disjoint
paths in ¿
1
and a set of unrouted demands in ¿.This entire
procedure is iteratively repeated on the SLDs remaining in ¿
to create the other elements of the partition,¿
2
;:::;¿
k
,until
all the demands in ¿ are successfully routed.
Since we are creating a partition of SLDs (not individual
lightpaths) we cannot assume that only one wavelength is
needed for each element of the partition.Since all the SLDs
in ¿
i
are mutually disjoint,their respective lightpaths can be
4
Here,each static lightpath demand represents a single lightpath which is
to be set up permanently.As a result,each demand is deﬁned only by its
source and destination nodes.
TABLE I
EXAMPLE
SLD
i
s
i
d
i
n
i
®
i
!
i
SLD
1
4
3
5
1:00
6:00
SLD
2
4
2
10
2:00
6:00
SLD
3
4
1
9
2:00
7:00
SLD
4
1
3
7
1:00
2:00
assigned the same set of wavelengths.On the other hand,each
individual lightpath of a particular SLD must be assigned a
different wavelength since they are all routed on the same path.
It follows that the number of wavelengths W
i
which must be
assigned to ¿
i
is the maximum number of lightpaths any SLD
included in ¿
i
requests.Wavelength assignment is performed
in the following manner.For each SLD in ¿
1
,its corresponding
lightpaths are assigned wavelengths 1,2,...up to W
1
if nec
essary.The lightpaths routed in ¿
2
are assigned wavelengths
f(W
1
+1);:::;(W
1
+W
2
)g,the lightpaths in ¿
3
are assigned
wavelengths f((W
1
+W
2
) +1);:::;((W
1
+W
2
) +W
3
)g,and
so on.Generally speaking,each element ¿
i
;i = 1;:::;k is
assigned wavelengths f(
P
i¡1
t=0
W
t
+1);:::;(
P
i¡1
t=0
W
t
+W
i
)g,
where W
0
= 0.
This method of wavelength assignment is the motivation
for sorting the SLDs in ¿ in decreasing order of the number
of lightpaths each SLD requests.Recall that the number of
wavelengths W
i
which must be assigned to ¿
i
is the maximum
number of lightpaths any SLD included in ¿
i
requests.If such
is the case,it is evident that it is more desirable to route
SLDs which request a large number of lightpaths (i.e.the
requests with high trafﬁc demands) in the same element of
the partition.In most cases,this will lead to a smaller number
of total wavelengths assigned,as will be demonstrated on an
example.This also means that high trafﬁc demands are routed
on mutually edge/time disjoint paths.We can intuitively see
that this will reduce congestion as opposed to routing high
trafﬁc on the same path at the same time.
Furthermore,the SLDs with the same number of lightpaths
are sorted in decreasing order of the lengths of their corre
sponding shortest paths in G.This is done since SLDs which
have longer shortest paths are generally harder to route and
should therefore be routed when more edges are available.
Related work is given in [20].If there are multiple SLDs
with the same number of lightpaths and the same shortest
path length,they are placed in random order.
To demonstrate the beneﬁt of sorting the SLDs before
creating a partition of ¿,a short example is considered.
Suppose the set of SLDs in Table I and the physical network
shown in Fig.1.(a).Let the upper bound h on the physical
length of a lightpath to be set to 2.The lightpaths of SLD
1
,
SLD
2
,and SLD
3
all overlap in time,while the lightpaths
of SLD
4
are only in time conﬂict with those of SLD
1
.
Suppose we create a partition of ¿ in the order in which
the SLDs are shown in Table I.In that case,SLD
1
,SLD
2
and SLD
4
could be routed in the ﬁrst element of the
partition ¿
1
,while SLD
3
would require a second element
¿
2
,as shown in Fig.2.(a).Such a partition would require
W
1
+ W
2
= max(n
1
;n
2
;n
4
) + max(n
3
) = 10 + 9 = 19
wavelengths to perform wavelength assignment.Now consider
7
Fig.2.An example of a partition of a set of SLDs ¿ obtained using the
DP
RWA
SLDalgorithm(a) without sorting the SLDs and (b) with sorting
the SLDs.
routing the SLDs in descending order of their requested
lightpaths,i.e.fSLD
2
;SLD
3
;SLD
4
,SLD
1
g.This could
result in a partition as follows:¿
1
= fSLD
2
;SLD
3
;SLD
4
g
and ¿
2
= fSLD
1
g shown in Fig.2.(b).Such a partition would
require W
1
+W
2
= max(n
2
;n
3
;n
4
)+max(n
1
) = 10+5 = 15
wavelengths.
The pseudocode of DP
RWA
SLD follows.
Input and initialization:
G = (V;E);
¿ = fSLD
1
;:::;SLD
M
g,where SLD
i
= (s
i
;d
i
;n
i
;®
i
;!
i
);i =
1;:::;M;//the set of SLDs
h = max(diam(G);
p
jEj);
¸ = 0;//the number of wavelengths
i:= 0;//the number of elements in the partition
Begin:
Sort the SLDs in ¿ in decreasing order of their corresponding values of
n
i
.Sort requests with the equal values of n
i
in decreasing order of the
lengths of their shortest paths in G (if more than one request has the same
length place them in random order);
while ¿ is not empty do
i:= i +1;
¿
i
= fg;
P
¿
i
= fg;//paths of SLDs in ¿
i
for each SLD
j
2 ¿ in the sorted order do
G
0
= G;
for each SLD
k
2 ¿
i
do
if (®
j
· ®
k
·!
j
) or (®
k
· ®
j
·!
k
) then
//SLD
j
2 ¿ and SLD
k
2 ¿
i
overlap in time
Remove from G
0
all edges in P(SLD
k
);
end if
end for
Find shortest path P(SLD
j
) for SLD
j
in G
0
;
if the length of P(SLD
j
) is · h then
Add P(SLD
j
) to P
¿
i
and SLD
j
to ¿
i
;
end if
end for
W
i
= max value of n
j
of any SLD
j
2 ¿
i
;
For each SLD in ¿
i
,assign to their corresponding lightpaths wavelengths
(¸ +1);:::up to (¸ +W
i
) if needed;
¸:= ¸ +W
i
;
¿:= ¿n¿
i
;
end while
End
B.DP
RWA
SLD*
A related version of the DP
RWA
SLD algorithm is also
proposed,referred to as DP
RWA
SLD
¤
.After creating an
element of the partition ¿
i
,a second attempt at routing into
¿
i
the SLDs remaining in ¿ is executed.The basic idea is the
following.After creating each element of the partition ¿
i
and
assigning up to W
i
wavelengths to each of the lightpaths of
the SLDs included in ¿
i
,we can see that there may be several
SLDs that require less than W
i
wavelengths.The edges on
paths used by these SLDs could be utilized by routing other
SLDs using the wavelengths assigned to ¿
i
but not used on
these particular edges.In other words,we want to “ﬁll up” ¿
i
by fully utilizing the set of wavelengths already assigned to
it.
This is best shown on an example.Suppose we created
an element ¿
i
which is assigned W
i
= 10 wavelengths.
Now suppose demand SLD
j
routed in ¿
i
requests 4 light
paths (i.e.n
j
= 4).These lightpaths are assigned wave
lengths (
P
i¡1
t=0
W
t
+1),(
P
i¡1
t=0
W
t
+2),(
P
i¡1
t=0
W
t
+3) and
(
P
i¡1
t=0
W
t
+4).Each edge on path P(SLD
j
) could be used
to route any SLD which demands (W
i
¡n
j
) = 10 ¡4 = 6
lightpaths or less even if it overlaps in time with SLD
j
.These
lightpaths would simply be assigned wavelengths (
P
i¡1
t=0
W
t
+
5),(
P
i¡1
t=0
W
t
+6),...up to (
P
i¡1
t=0
W
t
+10) if necessary.
To successfully execute this modiﬁcation,the following
steps are added to algorithm DP
RWA
SLD giving rise to
DP
RWA
SLD
¤
.After creating an element ¿
i
and assigning
W
i
wavelengths in the same way as DP
RWA
SLD (i.e.one
run of the while loop),we try and route the SLDs remaining
in ¿ a second time.As before,to route SLD
j
2 ¿ in ¿
i
we
start with graph Gand check to see if it is in time conﬂict with
any of the SLDs already routed in ¿
i
.For the SLDs which are
in time conﬂict with SLD
j
and request more than (W
i
¡n
j
)
lightpaths,we delete the edges of their corresponding paths
from G creating G
0
.The edges of those paths whose SLDs
request (W
i
¡n
j
) or less lightpaths remain in G
0
even though
they are in time conﬂict with SLD
j
.
SLD
j
is then routed on its shortest path P(SLD
j
) in G
0
.
If the routing is successful (i.e.there exists such a path and
its length is · h),SLD
j
is added to ¿
i
and removed from ¿.
In order to assign wavelengths to the lightpaths of SLD
j
,we
do the following.We check all the edges in path P(SLD
j
)
and determine the highest wavelength W
max
(P(SLD
j
))
used on any of these edges by an SLD in ¿
i
which
overlaps in time with SLD
j
.We then assign wavelengths
(W
max
(P(SLD
j
)) + 1);:::;(W
max
(P(SLD
j
)) + n
j
) to
the n
j
lightpaths of SLD
j
.Note that W
max
(P(SLD
j
)) is
the highest wavelength assigned to some demand SLD
k
2 ¿
i
whose path overlaps with P(SLD
j
) and can therefore
be written as (
P
i¡1
t=0
W
t
+ n
k
).Since prior to routing
SLD
j
,we deleted from G all those edges used by SLDs in
time conﬂict with SLD
j
requesting more than (W
i
¡ n
j
)
lightpaths,we can be certain that n
k
· W
i
¡ n
j
.It follows
that W
max
(P(SLD
j
)) + n
j
=
P
i¡1
t=0
W
t
+ n
k
+ n
j
·
P
i¡1
t=0
W
t
+ W
i
.This proves that we have not assigned
to SLD
j
any wavelength aside from the W
i
wavelengths
already assigned to ¿
i
.
8
The pseudocode of DP
RWA
SLD
¤
follows:
Input and initialization:
G = (V;E);
¿ = fSLD
1
;:::;SLD
M
g,where
SLD
i
= (s
i
;d
i
;n
i
;®
i
;!
i
);i = 1;:::;M;//the set of SLDs
h = max(diam(G);
p
jEj);
¸ = 0;//the number of wavelengths
i:= 0;//the number of elements in the partition
fillingUp:= false;//this indicates if we are starting to create a partition
or ”ﬁlling it up”
Begin:
Sort the SLDs in ¿ in decreasing order of their corresponding values of
n
i
.Sort requests with the equal values of n
i
in decreasing order of the
lengths of their shortest paths in G (if more than one request has the same
length place them in random order);
while ¿ is not empty do
if fillingUp == false then
Run one while loop of the DP
RWA
SLD algorithm;
fillingUp:= true;
else
for each SLD
j
2 ¿ in the sorted order do
G
0
= G
for each SLD
k
2 ¿
i
do
if (®
j
· ®
k
·!
j
) or (®
k
· ®
j
·!
k
) then
//SLD
j
2 ¿ and SLD
k
2 ¿
i
overlap in time
if n
k
> W
i
¡n
j
then
Remove from G
0
all edges in P(SLD
k
);
end if
end if
end for
Find shortest path P(SLD
j
) for SLD
j
in G
0
;
if the length of P(SLD
j
) is · h then
Add P(SLD
j
) to P
¿
i
and SLD
j
to ¿
i
;
Find the max wavelength W
max
(P(SLD
j
)) used by any SLD
in ¿
i
which uses any of the edges in P(SLD
j
) and is in time
conﬂict with SLD
j
;
Assign to SLD
j
the wavelengths (W
max
(P(SLD
j
)) +
1);:::;(W
max
(P(SLD
j
)) +n
j
);
end if
end for
fillingUp:= false;
¿:= ¿n¿
i
;
end if
end while
End
C.Complexity Analysis
The computational complexity of the DP
RWA
SLD and
DP
RWA
SLD
¤
algorithms follows.The DP
RWA
SLD
algorithm ﬁrst ﬁnds the allpairs shortest paths between nodes
in the physical network using Floyd’s algorithm [21] in
O(jV j
3
) time.The M SLDs are then sorted in O(Mlog M)
time.The while loop runs O(M
2
jV j
2
) time giving us a
ﬁnal complexity of O(jV j
3
+ Mlog M + M
2
jV j
2
).In the
DP
RWA
SLD
¤
algorithm,the while loop is run twice as
many times as in DP
RWA
SLD which still yields the
same complexity.The complexity of the DP
RWA
SLD and
DP
RWA
SLD
¤
algorithms is not comparable to that of
the TS
cg
=GGC and TS
cn
=GGC algorithms since the former
are constructive heuristics which end deterministically,while
the later are improvement heuristics which can be terminated
at any time and still obtain a feasible solution.However,
numerical results (see Section VII) indicate that in order to
obtain good solutions using the tabu search algorithms,a fair
number of iterations need to be run resulting in execution times
drastically longer than those of the greedy algorithms.
VI.LOWER BOUNDS
Since the algorithms considered in this paper are heuristics
which obtain upper bounds on the minimal objective function
values,it is useful to have good lower bounds in order to
assess the quality of the suboptimal solutions.A simplistic
lower bound on the number of wavelengths needed to perform
successful routing and wavelength assignment on a set of
scheduled lightpath demands such that the group lightpath
constraint is satisﬁed is
W
LB
n
max
= max
i=1;:::;M
fn
i
g:(1)
This represents the maximum number of lightpaths requested
by any SLD in ¿.However,this lower bound is not necessarily
efﬁcient for a set of lightpath requests highly correlated
in time.In [22],a simple lower bound on the number of
wavelengths required to set up a regular virtual topology in
wavelength routed optical networks is obtained by comparing
the ﬁxed logical degree to the maximum physical degree in
the network.We further develop this idea of the logical to
physical degree ratio to derive a tighter lower bound for the
RWA SLD problem as follows.
Let
S
s
= fSLD
i
js
i
= s;i = 1;:::;Mg;8s 2 V (2)
be the set of SLDs whose source node is node s.
Let
T
S
s
= f®
i
[!
i
jSLD
i
2 S
s
;i = 1;:::;Mg;8s 2 V (3)
be an ordered set of moments in time when some SLD in
S
s
is either set up and/or some SLD in S
s
is torn down.
If T
S
s
= ft
s
1
;:::;t
s
jT
S
s
j
g,then t
s
1
< t
s
1
:::< t
s
jT
S
s
j
and
jT
S
s
j · 2jS
s
j.
Let
TO
S
s
j
= fSLD
k
j[t
s
j
;t
s
j+1
] µ [®
k
;!
k
];SLD
k
2 S
s
g;
8s 2 V;8j = 1;:::;jT
S
s
j ¡1;
(4)
be the set of SLDs whose source node is s and are active in
time interval [t
s
j
;t
s
j+1
].This means that all the SLDs in TO
S
s
j
overlap in time.Furthermore,let TO
S
s
j
be an ordered set with
respect to the number of lightpaths requested by each SLD.In
other words,if TO
S
s
j
= fSLD
to
s
j
1
;:::;SLD
to
s
j
jTO
S
s
j
j
g,then
n
to
s
j
1
< n
to
s
j
2
<:::< n
to
s
j
jTO
S
s
j
j
.
Lastly,let ¢
phy
s
be the outdegree
5
of node s in the physical
topology.All the lightpaths of the SLDs in S
s
will surely be
routed over one of the ¢
phy
s
outgoing edges adjacent to node
s.If the individual lightpaths of a single SLD do not neces
sarily need to be routed on the same path (i.e.if we relax the
group lightpath constraint),each individual lightpath can be
routed over any one of the ¢
phy
s
outgoing edges.Lightpaths
in S
s
which overlap in time,i.e.their respective SLDs are both
in at least one set TO
S
s
j
,j = 1;:::;jT
S
s
j ¡1,and which are
routed over the same physical edge must be assigned different
5
According to our problem deﬁnition,the physical outdegree is equal to
the physical indegree for each node in V since we assume that each link in
the physical topology represents two ﬁbers  one in each direction.
9
wavelengths.To route and assign wavelengths to the lightpaths
of the SLDs in some set TO
S
s
j
,at least one physical link will
have
W
LB
TO
S
s
j
=
&
P
ijSLD
i
2TO
S
s
j
n
i
¢
phy
s
'
;
8s 2 V;8j 2 f1;:::;jT
S
s
j ¡1g;
(5)
lightpaths routed over it and therefore require at least as many
wavelengths.
If we consider the lightpaths of the SLDs in set TO
S
s
j
to represent a logical topology over the physical topology
which is constant in the corresponding time interval,W
LB
TO
S
s
j
represents the ratio of logical to physical degree of node s in
time interval [t
s
j
;t
s
j+1
].The highest such ratio
W
LB
S
= max
s2V
max
1·j·jT
S
s
j¡1
W
LB
TO
S
s
j
(6)
for any source node in the network over all time intervals
is a lower bound on the number of wavelengths needed
to perform routing and wavelength assignment for a set of
scheduled lightpath demands ¿.Note that W
LB
S
is a lower
bound for the RWA SLD problem where the group lightpath
constraint is relaxed.Since imposing such a constraint makes
the problem harder,W
LB
S
is also a lower bound for the
constrained problem.
Furthermore,assuming the group lightpath constraint does
apply,we suggest an alternative lower bound,referred to as
W
LB0
S
.Let the load of SLD
i
be its corresponding number
of lightpaths n
i
.A lower bound W
LB0
TO
S
s
j
on the number
of wavelengths needed to perform routing and wavelength
assignment of the SLDs in set TO
S
s
j
is the maximum load
on any outgoing physical link adjacent to s after performing
optimal load balancing of the jTO
S
s
j
j SLDs over the ¢
phy
s
links.If n
i
= 1 for all SLDs in TO
S
s
j
,load balancing is
trivial and gives the same lower bound as (6).Otherwise,this
problem is NPComplete.For very small cases,exhaustive
search could be applied.However,for larger cases this is
not practical.Since we do not actually need to perform load
balancing but are solely interested in the maximum load of the
optimal solution,we can use a lower bound on the maximum
load,which,in turn,is a lower bound on the number of
wavelengths needed.We know that at least
N
S
s
j
=
&
jTO
S
s
j
j
¢
p
s
'
;8s 2 V;8j 2 f1;:::;jT
S
s
j ¡1g (7)
SLDs (not individual lightpaths) will surely be routed on at
least one physical outgoing link adjacent to s in time period
[t
s
j
;t
s
j+1
].Deﬁned as such,N
S
s
j
· jTO
S
s
j
j.By summing
up the load of the N
S
s
j
SLDs in TO
S
s
j
with the lightest
load,i.e.the lowest number of lightpaths n
i
,we obtain a
lower bound on the maximum load.Since TO
S
s
j
is a set of
SLDs sorted in nondecreasing order of their corresponding
number of SLDs,the lower bound on the number of lightpaths
routed over at least one of the outgoing edges of s in time
interval [t
s
j
;t
s
j+1
] is the sum of the number of lightpaths
of the ﬁrst N
S
s
j
SLDs in TO
S
s
j
.In other words,if TO
S
s
j
=
fSLD
to
s
j
1
;:::;SLD
to
s
j
jTO
S
s
j
j
g,then
W
LB0
TO
S
s
j
=
N
S
s
j
X
i=1
n
to
s
j
i
;8s 2 V;8j 2 f1;:::;jT
S
s
j ¡1g:
(8)
It follows that the lower bound on the number of wavelengths
needed to perform RWA of a set of scheduled lightpath
demands in the case that the group lightpath constraint applies
is
W
LB0
S
= max
s2V
max
1·j·jT
S
s
j¡1
W
LB0
TO
S
s
j
:(9)
Note that for some cases,e.g.when one or a few SLDs request
a very large number of lightpaths,bounds (1) and/or (6) may
be tighter.As a result,we consider all the mentioned bounds.
The above discussion regarding lower bounds derived by
considering SLDs with common source nodes can also be
applied to SLDs with common destination nodes.Namely,if
SLDs terminate at the same node d 2 V,they will surely be
routed over one of the ¢
phy
d
indegree edges adjacent to node
d.Let
D
d
= fSLD
i
jd
i
= d;i = 1;:::;Mg;8d 2 V (10)
be the set of SLDs whose destination node is d.This is
analogous to (2) for SLDs with source node s.Sets T
D
d
j
and
TO
D
d
representing the time intervals and time overlapping
SLDs in D
d
can be obtained from (3) and (4),respectively,
by replacing S with D and s with d.N
D
d
j
can be obtained in
the same manner from (7).This leads to two additional lower
bounds,
W
LB
D
= max
d2V
max
1·j·jT
D
d
j¡1
W
LB
TO
D
d
j
=
max
d2V
max
1·j·jT
D
d
j¡1
8
<
:
2
6
6
6
P
ijSLD
i
2TO
D
d
j
n
i
¢
phy
d
3
7
7
7
9
=
;
(11)
and
W
LB0
D
= max
d2V
max
1·j·jT
D
d
j¡1
W
LB0
TO
D
d
j
=
max
d2V
max
1·j·jT
D
d
j¡1
8
>
<
>
:
N
D
d
j
X
i=1
n
to
d
j
i
9
>
=
>
;
(12)
analogous to (6) and (9).
The preceding discussion shows a lower bound on the num
ber of wavelengths needed to solve the RWA SLD problem
without the group lightpath constraint to be
W
LB
= maxfW
LB
S
;W
LB
D
g:(13)
For the problem augmented with the group lightpath con
straint,a tighter lower bound is
W
0
LB
= maxfW
LB
n
max
;W
LB
S
;W
LB0
S
;W
LB
D
;W
LB0
D
g:(14)
In the example given in Table I,supposing the physi
cal topology shown in Fig.1.(a),the lower bound W
0
LB
would be calculated as follows.In this example,¿ =
10
fSLD
1
;SLD
2
;SLD
3
;SLD
4
g,M = 4,and V = f1;2;3;4g,
while the physical in and outdegree of each node is ¢
phy
i
=
2,8i 2 V.Lower bound W
LB
n
max
= 10 represents the
maximum number of lightpaths requested by any SLD in
¿.To calculate W
LB
S
we must ﬁnd W
LB
TO
S
s
j
for each s and
j.For s = 1,S
1
= fSLD
4
g,while for s = 4,S
4
=
fSLD
1
;SLD
2
;SLD
3
g.For s = 2 or s = 3,these sets are
empty since nodes 2 and 3 are not source nodes for any
requested SLD.T
S
1
= f1:00,2:00g and T
S
4
=f1:00,2:00,
6:00,7:00g,while TO
S
1
1
= fSLD
4
g,TO
S
4
1
= fSLD
1
g,
TO
S
4
2
= fSLD
1
;SLD
3
;SLD
2
g,and TO
S
4
3
= fSLD
3
g.
Note that these sets are ordered in nondecreasing order of
the number of lightpaths requested by the SLDs in the set.
Lower bounds over the source nodes and time intervals are
W
LB
TO
S
1
1
= d7=2e = 4,W
LB
TO
S
4
1
= d5=2e = 3,W
LB
TO
S
4
2
=
d(5+10+9)=2e = 12 and W
LB
TO
S
4
3
= d9=2e = 5.It follows that
W
LB
S
= 12.Furthermore,N
S
1
1
= 1,N
S
4
1
= 1,N
S
4
2
= 2,and
N
S
4
3
= 1.It follows that W
LB0
TO
S
1
1
= n
4
= 7,W
LB0
TO
S
4
1
= n
1
= 5,
W
LB0
TO
S
4
2
= n
1
+ n
3
= 5 + 9 = 14,and W
LB0
TO
S
4
3
= n
2
= 10.
This leads to lower bound W
LB0
S
= 14.W
LB
D
and W
LB0
D
are analogously found to be 6 and 10 respectively.It follows
that a lower bound for the RWA SLD without the group
lightpath constraint is W
LB
= maxf12;6g = 12,while
W
0
LB
= maxf10;12;14;6;10g = 14 gives a lower bound for
the constrained version of the problem.In the example in Fig.
2.(b),we can see that a routing and wavelength assignment
was found with 15 wavelengths,demonstrating the efﬁciency
of the bound for this case.
VII.ANALYSIS OF COMPUTATIONAL RESULTS
A.Experimental method and numerical results
The TS
cg
=GGC [9],TS
cn
=GGC,DP
RWA
SLD,and
DP
RWA
SLD
¤
algorithms for the Routing and Wave
lengths Assignment problemof Scheduled Lightpath Demands
were all implemented in C++ and run on a PC powered by a
P4 2.8GHz processor.The TS
cg
[9] and the suggested TS
cn
tabu search algorithms for the routing subproblem were run
in combination with the GGC graph coloring algorithm from
[10] for wavelength assignment.The source code for the GGC
algorithmwas provided by the authors.Randomnumbers were
generated using the R250 random number generator [23].
We tested the algorithms using the hypothetical U.S.back
bone given in [9].The network consists of 29 nodes and 44
edges which are assumed to be bidirectional.The weight of
an edge represents its physical length.Using a Perl script
provided by the authors of [9],60 sets of M=30 SLDs were
generated with time correlation 0:01,and 60 sets with time
correlation 0:8.Each SLD could request at most 10 lightpaths.
Time correlation closer to 0 means that the SLDs are weakly
time correlated while time correlation closer to 1 means that
the SLDs generated are strongly time correlated.For exact
deﬁnition of the time correlation parameter used,refer to [9].
In this paper,we will refer to this parameter as ±.
As in [9],the TS
cg
=GGC algorithm was run with a
neighborhood size of 200,the length of the tabu list was set
to 2 times the neighborhood size and the number of allowed
iterations without improvement was set to 150.Regarding the
TS
cn
=GGC algorithm,the size of the neighborhood is not an
input parameter since TS
cn
uses an adaptive neighborhood.
The remaining parameters for the TS
cn
=GGC algorithm were
determined experimentally.Since effective tabu tenures,i.e.
the length of the tabu list,have been shown to depend on the
size of the problem [13],we tested the algorithm with tabu
tenures proportional to the number of possible neighboring
solutions.Since a neighboring solution with respect to a
current one is deﬁned such that one of the M SLDs is
routed on a different path,there are M(K ¡ 1) possible
neighbors.Experimental results indicated that a tabu list of size
M(K ¡1)=10 was long enough to disable cycling and short
enough so as not to restrict the search.Setting the number of
iterations without improvement to a value dependant on the
size of the problem also proved effective.Empirical testing
also showed that applying diversiﬁcation every M(K ¡1)=3
iterations helped obtain good results.
Both the TS
cg
=GGC and TS
cn
=GGC algorithms were run
for 3000 iterations,as in [9],and K ranged from 2 to 5.
Since a tabu search algorithm can reach its best incumbent
solution in any iteration and then continue running without
any improvement (even with diversiﬁcation),we recorded the
iteration in which the best solution was ﬁrst found for each
test case for both tabu search algorithms.We also measured
the average execution time per iteration and the time it took
each tabu search algorithm to reach its best solution.These
results,averaged over the 60 test cases,and the average
number of wavelengths of the solutions obtained by each of
the tabu search algorithms for time correlations 0.01 and 0.8
are shown in Tables II and III,respectively.The number of
wavelengths and execution times for the DP
RWA
SLD and
DP
RWA
SLD
¤
algorithms and lower bound W
0
LB
are also
shown in Tables II and III.For further insight regarding exe
cution time,in Table IV,the number of iterations and the time
it took to reach the best solution by each of the tabu search
algorithms for the test case for which they performed worst
are shown.Note that the results shown regarding the execution
times of the the tabu search algorithms do not include the time
it takes to subsequently run the GGC algorithm.The average
execution times of the GGC algorithm were around 12 and
18 seconds for time correlations 0.01 and 0.8,respectively.
We can see that TS
cn
=GGC performs better than (or equal
to) TS
cg
=GGC in all cases with respect to solution quality
and execution time.For test data with time correlation 0.01,
the initial solution is often optimal since most of the SLDs
do not overlap in time.These test cases,although helpful in
showing the beneﬁt of performing RWA considering scheduled
lightpath demands as opposed to static lightpath demands,
are less effective in comparing the results of RWA SLD
algorithms.The results for time correlation 0.8 are much
more interesting.The speciﬁc test cases where the number
of wavelengths differed in the solutions obtained by each of
the tabu search algorithms are shown in Fig.3.We can see
that TS
cn
=GGC used less wavelengths in all cases.
According to Table II,the DP
RWA
SLD algorithm
outperforms both tabu search algorithms in combination with
the GGC algorithm for time correlation 0.01.For time cor
11
TABLE II
HYPOTHETICAL U.S.NETWORK [9],± = 0:01,M = 30:AVG.NO.OF WAVELENGTHS,AVG.ITER.IN WHICH THE BEST SOLUTION WAS OBTAINED,AVG.
EXEC.TIME PER ITERATION AND AVG.EXEC.TIME TO BEST SOLUTION FOR ALGORITHMS TS
cg
=GGC [9] AND TS
cn
=GGC;AVG.NO.OF
WAVELENGTHS AND AVG.EXEC.TIME FOR ALGORITHMS DP
RWA
SLD AND DP
RWA
SLD
¤
,AND LOWER BOUND W
0
LB
.
TS
cg
=GGC [9]
TS
cn
=GGC
Lower bound
K
Avg.wave
lengths
Avg.iter.
found best
Avg.time/iter
(ms)
Avg.time to
best sol.(ms)
Avg.wave
lengths
Avg.iter.
found best
Avg.time/iter
(ms)
Avg.time to
best sol.(ms)
Avg.W
0
LB
2
11.12
0.81
395.37
85.74
11.12
3.53
4.48
27.57
3
10.50
12.25
402.11
4918.09
10.50
2.72
2.73
10.44
4
10.28
35.38
402.12
14204.34
10.28
9.92
2.69
33.71
5
10.22
26.63
495.22
10901.78
10.22
10.52
2.59
38.65
9.90
DP
RWA
SLD
DP
RWA
SLD
¤
Avg.wavelengths
Avg.execution time (ms)
Avg.wavelengths
Avg.execution time (ms)
10.00
0.76
9.90
0.88
TABLE III
HYPOTHETICAL U.S.NETWORK [9],± = 0:8,M = 30:AVG.NO.OF WAVELENGTHS,AVG.ITER.IN WHICH THE BEST SOLUTION WAS OBTAINED,AVG.
EXEC.TIME PER ITERATION AND AVG.EXEC.TIME TO BEST SOLUTION FOR ALGORITHMS TS
cg
=GGC [9] AND TS
cn
=GGC;AVG.NO.OF
WAVELENGTHS AND AVG.EXEC.TIME FOR ALGORITHMS DP
RWA
SLD AND DP
RWA
SLD
¤
,AND LOWER BOUND W
0
LB
.
TS
cg
=GGC [9]
TS
cn
=GGC
Lower bound
K
Avg.wave
lengths
Avg.iter.
found best
Avg.time/iter
(ms)
Avg.time to
best sol.(ms)
Avg.wave
lengths
Avg.iter.
found best
Avg.time/iter
(ms)
Avg.time to
best sol.(ms)
Avg.W
0
LB
2
14.33
28.48
396.90
11329.26
13.85
77.42
7.66
595.97
3
13.78
59.92
399.40
37730.77
12.65
13.82
4.21
59.90
4
12.47
240.12
404.43
98217.44
11.68
36.45
4.03
147.18
5
11.70
321.03
404.24
130825.00
11.20
96.38
3.89
368.59
10.08
DP
RWA
SLD
DP
RWA
SLD
¤
Avg.wavelengths
Avg.execution time (ms)
Avg.wavelengths
Avg.execution time (ms)
11.90
0.94
10.63
1.07
TABLE IV
HYPOTHETICAL U.S.NETWORK [9],M = 30,WORST CASES:TEST CASES FOR WHICH THE BEST SOLUTION WAS FOUND IN THE HIGHEST ITERATION,
THE CORRESPONDING ITERATION,AVG.EXECUTION TIME PER ITERATION AND THE EXECUTION TIME TO BEST SOLUTION FOR ALGORITHMS
TS
cg
=GGC [9] AND TS
cn
=GGC
TS
cg
=GGC [9]
TS
cn
=GGC
±
K
Test case
Iteration
found best
Avg.time/iter.
(s)
Time to best
solution (s)
Test case
Iteration
found best
Avg.time/iter.
(s)
Time to best
solution (s)
2
52
2
0.3921
0.784
54
86
0.0077
0.666
0.01
3
21
158
0.4059
64.137
21
45
0.0047
0.210
4
21
277
0.4035
111.770
21
141
0.0044
0.624
5
24
265
0.4012
106.344
21
149
0.0045
0.671
2
39
1509
0.3965
598.240
39
1348
0.0078
10.467
0.8
3
22
1204
0.4006
482.276
17
219
0.0051
1.112
4
41
2419
0.4020
972.498
39
457
0.0036
1.649
5
33
2989
0.4041
1207.822
54
1525
0.0042
6.442
relation 0.8,DP
RWA
SLD outperforms TS
cg
=GGC for
cases where K = 2,3,and 4,and outperforms TS
cn
=GGC
for cases where K = 2 and 3.DP
RWA
SLD has the
shortest execution time among all the mentioned algorithms
for all cases.The DP
RWA
SLD
¤
algorithm outperforms
TS
cn
=GGC,TS
cg
=GGC and DP
RWA
SLD for all val
ues of K in solution quality and both tabu search algo
rithms in execution time.Since the TS
cn
=GGC algorithm
uses less wavelengths than TS
cg
=GGC for all test cases,
and the DP
RWA
SLD
¤
algorithm uses less wavelengths
than the DP
RWA
SLD algorithm in all cases,we com
pare the results of TS
cn
=GGC and DP
RWA
SLD
¤
.The
test cases where the solutions obtained by TS
cn
=GGC and
DP
RWA
SLD
¤
differed for time correlation 0.8 are shown
in Fig.4.The TS
cn
=GGC algorithm performed better in 4
cases,while the DP
RWA
SLD
¤
algorithmperformed better
TABLE V
HYPOTHETICAL U.S.NETWORK [9],M = 30:AVERAGE NEIGHBORHOOD
SIZE FOR ALGORITHM TS
cn
=GGC
Average neighborhood size
K
± = 0:01
± = 0:8
2
0.810
2.035
3
0.711
1.946
4
0.662
1.879
5
0.661
1.825
in 14 cases.
Since the neighborhood of the TS
cn
=GGC algorithm is
adaptive,we recorded the average neighborhood sizes for
the TS
cn
=GGC algorithm.These results are shown in Table
V.We can see that the proposed neighborhood reduction
technique dramatically reduces the size of the neighborhood
12
TABLE VI
HYPOTHETICAL U.S.NETWORK [9],M = 30:AVG.PHYSICAL HOP LENGTH OF THE LIGHTPATHS IN THE SOLUTIONS OBTAINED BY ALGORITHMS
TS
cg
=GGC [9],TS
cn
=GGC,DP
RWA
SLD AND DP
RWA
SLD
¤
Time correlation ± = 0:01
Time correlation ± = 0:8
K
TS
cg
=GGC[9]
TS
cn
=GGC
DP
RWA
SLD
DP
RWA
SLD
¤
TS
cg
=GGC[9]
TS
cn
=GGC
DP
RWA
SLD
DP
RWA
SLD
¤
2
3.755
3.828
3.847
3.987
3
3.888
3.788
3.818
3.819
3.983
4.001
3.980
3.993
4
4.006
3.876
4.468
4.167
5
4.032
3.912
4.631
4.248
0
2
4
6
8
10
12
14
16
18
20
3 10 14 18 19 22 24 31 40 46 48 56 60
Test Case
NumberofWavelengths
TScg/GGC [9]
TScn/GGC
Lower Bound
Fig.3.Hypothetical U.S.network [9],± = 0:8,M = 30:The number
of wavelengths of the solutions obtained by algorithms TS
cg
=GGC [9] and
TS
cn
=GGC,and lower bound W
0
LB
for the test cases where the number of
wavelengths differ.
and yet obtains good results.The average neighborhood size
for test cases with time correlation 0.01 is less than one since
for many of the test cases,solutions can be found where none
of the SLDs overlap in both time and space due to the very
small time correlation.Such solutions give conﬂict graphs
where none of the nodes representing lightpaths of different
SLDs are adjacent,and thus RWA is trivial.
The average physical hop lengths of the lightpaths estab
lished by each of the algorithms are shown in Table VI.For test
cases with time correlation 0.01,the TS
cn
=GGC algorithm
established shorter lightpaths than the TS
cg
=GGC algorithm
for cases where K = 3,4,and 5.The DP
RWA
SLD
¤
set
up shorter lightpaths than the tabu search algorithms for all
cases but K = 2 for TS
cg
=GGC and K = 3 for TS
cn
=GGC.
For test cases with time correlation 0.8,the TS
cn
=GGC and
DP
RWA
SLD
¤
algorithms were better than TS
cg
=GGC
for K = 4 and 5,while the latter performed better for K = 2
and 3.The DP
RWA
SLDalgorithmestablished the shortest
lightpaths for both time correlations.
The algorithms were also tested on a reference European
core network topology shown in Fig.5 which was designed
as part of the COST Action 266 project [24].This network
consists of 14 nodes and 39 edges.20 test cases with time
correlation ± = 0:95 and M = 200 SLDs were generated,
where each SLD can request at most 10 lightpaths.The
tabu search algorithms were run with K = 5.The average
number of wavelengths and the average execution times to
reach the best solution are shown in Table VII.For the
European network,all three proposed algorithms signiﬁcantly
0
2
4
6
8
10
12
14
16
18
20
3 10 11 14 15 18 19 20 22 24 26 29 39 42 45 48 50 53
Test Case
NumberofWavelengths
TScn/GGC
DP_RWA_SLD*
Lower Bound
Fig.4.Hypothetical U.S.network [9],± = 0:8,M = 30:The number
of wavelengths of the solutions obtained by algorithms TS
cn
=GGC and
DP
RWA
SLD
¤
,and lower bound W
0
LB
for the test cases where the
number of wavelengths differ.
outperform the TS
cg
=GGC algorithm with respect to both
the number of wavelengths and execution time
6
.The wave
lengths required for the speciﬁc test cases are shown in
Fig.6.The average neighborhood size for the TS
cn
=GGC
algorithm was 1.540.The average physical hop lengths of
the established lightpaths were as follows:3.503,3.655,
2.717 and 2.730 for algorithms TS
cg
=GGC,TS
cn
=GGC,
DP
RWA
SLD and DP
RWA
SLD
¤
,respectively.Here,
TS
cg
=GGC outperformed TS
cn
=GGC,but DP
RWA
SLD
and DP
RWA
SLD
¤
again established shorter lightpaths
than the tabu search algorithms.
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Vi enna
Pr ague
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Fr ankf ur t
Zagr eb
Fig.5.The hypothetical European core network from [24].
6
The run time for the GGC algorithm for the test cases generated for the
European network was about 220 seconds.
13
TABLE VII
HYPOTHETICAL EUROPEAN NETWORK [24],± = 0:95,M = 200:AVG.NO.OF WAVELENGTHS,AVG.ITER.IN WHICH THE BEST SOLUTION WAS
OBTAINED,AVG.EXEC.TIME PER ITERATION AND AVG.EXEC.TIME TO BEST SOLUTION FOR ALGORITHMS TS
cg
=GGC [9] AND TS
cn
=GGC;AVG.
NO.OF WAVELENGTHS AND AVG.EXEC.TIME FOR ALGORITHMS DP
RWA
SLD AND DP
RWA
SLD
¤
,AND LOWER BOUND W
0
LB
.
TS
cg
=GGC [9]
TS
cn
=GGC
Lower bound
K
Avg.wave
lengths
Avg.iter.
found best
Avg.time/iter
(s)
Avg.time to
best sol.(s)
Avg.wave
lengths
Avg.iter.
found best
Avg.time/iter
(s)
Avg.time to
best sol.(s)
Avg.W
0
LB
5
29.00
935.40
1.403
1301.228
22.70
905.00
0.076
69.837
DP
RWA
SLDC
DP
RWA
SLD
¤
13.05
Avg.wavelengths
Avg.execution time (s)
Avg.wavelengths
Avg.execution time (s)
21.80
0.0203
19.45
0.0227
0
5
10
15
20
25
30
35
40
45
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Test Case
NumberofWavelengths
TScg/GGC [9]
TScn/GGC
DP_RWA_SLD
DP_RWA_SLD*
Lower Bound
Fig.6.Hypothetical European network [24],± = 0:95,M = 200:The number of wavelengths of the solutions obtained by TS
cg
=GGC [9],TS
cn
=GGC,
DP
RWA
SLD,and DP
RWA
SLD
¤
,and lower bound W
0
LB
.
B.Discussion
All three proposed algorithms give better quality solutions
in less time than the TS
cg
=GGC [9] algorithm for the data
tested in this paper.The proposed tabu search algorithm,
TS
cn
=GGC,uses less wavelengths than TS
cg
=GGC and
yet evaluates only a few neighbors in each iteration.The
very efﬁcient neighborhood reduction technique,in addition
to improving the quality of the solutions,drastically reduces
the execution time per iteration with respect to the previous art.
The time per iteration of the TS
cn
=GGC algorithmis not only
dramatically shorter than that of the TS
cg
=GGC algorithm,
but surprisingly decreases as K increases for the cases tested.
One of the reasons for this is that,for this data set,the average
neighborhood size decreased as K increased (see Table V).
The neighborhood size depends on the topology of the conﬂict
graph and is therefore dependent on K.Although,in general,
the neighborhood size does not necessarily decrease as K
increases,such was the case for the data instances evaluated in
this paper.Examining the behavior of the algorithm further,
we found that when K is small,it occurs more frequently
that all neighboring solutions are on the tabu list.In such
cases,alternative neighboring solutions outside the reduced
neighborhood set are examined until a valid neighbor is found.
This slightly increases the runtime of the algorithm.
Another point worth mentioning,regarding the TS
cn
=GGC
algorithm,is that the number of iterations required to reach
the best solution is higher when K = 2 than when K > 2.
Since neighborhood reduction is so drastic,the search is too
restrictive when K is very small.The search technique is much
more effective when K is larger,which is convenient since
these are the cases when the problem size is bigger and the
corresponding combinatorial optimization problem is harder.
Regarding the proposed greedy algorithms,both
DP
RWA
SLD and DP
RWA
SLD
¤
outperform
TS
cg
=GGC in all cases with respect to the number of
wavelengths and execution time.These algorithms also
establish shorter lightpaths.The DP
RWA
SLD and
DP
RWA
SLD
¤
algorithms are easy to implement,give
good quality solutions and can be applied to large networks
due to their very short execution times.DP
RWA
SLD
¤
is
negligibly slower and establishes slightly longer lightpaths
than DP
RWA
SLD,but performs signiﬁcantly better with
respect to the number of wavelengths used.
Although the greedy algorithm DP
RWA
SLD
¤
is bet
ter on average than the proposed tabu search algorithm
TS
cn
=GGC,for speciﬁc test cases this is sometimes not true
(see Figure 4 and 6).An effort was made to determine a pattern
in test cases in which the tabu search algorithm performed
better than the greedy algorithm,and vice versa.However
nothing conclusive was found.This is not surprising since
both strategies (greedy and tabu) are heuristics and the search
trajectory can be unpredictable depending on input data.If
the input data in an instance is such that a greedy strategy
provides an effective minimization direction,it is possible that
nothing better will be obtained by the improvement mechanism
of tabu search.Also,the initial solution used in tabu search can
sometimes be inefﬁcient (far away from the optimal solution),
14
in which case it might be difﬁcult to reach a very good
suboptimal solution via a restricted neighborhood search.In
some other instances,input data can be such that a good
initial solution and effective improvements are provided with
tabu search strategy,while a greedy strategy lacks ﬂexibil
ity in search directions and ends with an inferior solution.
Due to short computational times,for smaller problems both
TS
cn
=GGC and DP
RWA
SLD
¤
could be applied and the
better solution selected.For larger problems it might be better
to run the greedy algorithm,compare the solution with the
available lower bound,and in case of a signiﬁcant gap between
the solution and its lower bound,the tabu search algorithm
could be applied in an attempt to improve the solution.
VIII.CONCLUSION
In order to efﬁciently utilize resources in wavelengthrouted
optical networks,it is necessary to successfully solve the
problem of Routing and Wavelength Assignment.Scheduled
lightpath demands,where the setup and teardown times of
lightpaths are known a priori,could be considered by RWA
algorithms in order to utilize the network’s resources even
further.In this work,efﬁcient heuristic algorithms are pro
posed for the routing and wavelength assignment of scheduled
lightpath demands in networks with no wavelength converters.
Testing and comparing with an existing algorithmfor the RWA
SLD problem shows that these algorithms not only provide
solutions of better or equal quality,but are dramatically faster.
New lower bounds for the RWA SLD problem are also
proposed.Further avenues of research will include developing
similar algorithms for routing and wavelength assignment in
networks with full or limited wavelength conversion.Networks
equipped with a limited number of transmitters and receivers
at each node and/or a limited number of wavelengths on
each link will also be considered.Furthermore,routing and
wavelength assignment algorithms which consider physical
layer QoS (Quality of Service) demands,such as target BER
(Bit Error Rate) levels,could prove interesting research topics.
Fault tolerant RWA and restoration schemes for scheduled
lightpaths demands are important issues which could also be
addressed.
ACKNOWLEDGMENT
The author would like to thank J.Kuri for sending the
Perl script for generating test data,D.Kirovski for sending
the source code of the GGC algorithm,and the anonymous
referees for their insightful comments and helpful suggestions
which signiﬁcantly improved the quality and presentation of
this paper.
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Nina SkorinKapov was born in Zagreb,Croatia,in
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nications from the Faculty of Electrical Engineering
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the Department of Telecommunications at the same
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optical networks),network routing algorithms and network topology design.
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